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From |
"J Jones" <statafan@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Rescale using covariance matrix for weighted PCA? |

Date |
Wed, 21 May 2008 22:25:22 +0100 |

Hello--I would like to do weighted principal-components analysis (weighted PCA / WPCA; initialisms added for accessibility in future searches); however I am not sure how to do so. I have variables, which may be organized into sets if need be. I know how to get covariance matrices from factor analysis or PCA and I know how to do simple rescaling such as dividing an original score by a variable's standard deviation. Is it possible to rescale a variable by manipulating its covariance matrix? References say to weight variables from a particular set by dividing by the-reciprocal-of-the square-roo- of-the-eigenvalue-of-the-set's-first-principal-component (W)... and also dividing by the standard deviation of the variable. The exact formula they give is: Rescale score=(orig score)/(st dev*W). So the general method involves doing PCA on sets of variables and using the weighitng factor in order to confer proper weight to each set. THen you do facotr analysis on the rescaled scores. Can somebody help please? Thank you. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Rescale using covariance matrix for weighted PCA?***From:*"J Jones" <statafan@googlemail.com>

**Re: st: Rescale using covariance matrix for weighted PCA?***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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